import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


# 载入数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

#每个批次的大小
batch_size = 100

#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size  # // 整除

#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

#创建一个简单的神经网络
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W) + b)

#定义二次代价函数
loss = tf.reduce_mean(tf.square(y - prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()

# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1)) # argmax返回一维张量中最大的值所在的位置

#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


with tf.Session() as sess:
    sess.run(init)
    for epoch in range(1001):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})

        acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})

        print('Iter' + str(epoch) + ',Testing Accuracy' + str(acc))

        # Iter20,Testing Accuracy0.9142